Training for the marathon is a complex problem. In order to run an optimal time, runners must find the right workload for their current abilities and identify the correct balance between the hard work and rest throughout their training programmes. We propose a recommender system that will help guide runners through the weeks leading up to the marathon. Using a large sample of marathon training data (8730 runners), we generate user profiles that capture both a runner`s current fitness and training levels, and leverage this information to generate tailored recommendations for future weeks of training. We investigate patterns of successful runners to determine how best to schedule recommendations and training to allow for improvement in fitness levels alongside adequate rest.
© Copyright 2020 2020 Proceeding: RecSys '20: Fourteenth ACM Conference on Recommender Systems Virtual Event Brazil September, 2020. Published by Association for Computing Machinery. All rights reserved.